Abstract

To explore human deviations from Bayes rule in numerically
explicit problems, prior and likelihood probabilities or frequencies are
manipulated and their effects on posterior probabilities or surprisals are
measured. Results show that people use both priors and likelihoods in Bayesian
directions, but the effect of likelihood information is stronger than that of
prior information. Use of frequency information and surprisal measures increase
deviations from Bayesian predictions. There is evidence that people do compute
something like the standardizing marginal data term when asked for probability
estimates, but not when asked for surprisal ratings.